Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations6880
Missing cells25125
Missing cells (%)11.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory256.0 B

Variable types

Categorical8
Text11
DateTime2
Unsupported7
Numeric4

Alerts

ontime has constant value "G"Constant
delay has constant value "R"Constant
Driver_MobileNo is highly overall correlated with Market/Regular High correlation
Market/Regular is highly overall correlated with Driver_MobileNo and 2 other fieldsHigh correlation
Minimum_kms_to_be_covered_in_a_day is highly overall correlated with Market/Regular and 3 other fieldsHigh correlation
TRANSPORTATION_DISTANCE_IN_KM is highly overall correlated with customerID and 1 other fieldsHigh correlation
customerID is highly overall correlated with Minimum_kms_to_be_covered_in_a_day and 2 other fieldsHigh correlation
customerNameCode is highly overall correlated with Minimum_kms_to_be_covered_in_a_day and 3 other fieldsHigh correlation
vehicleType is highly overall correlated with Market/Regular and 2 other fieldsHigh correlation
GpsProvider is highly imbalanced (59.4%)Imbalance
Market/Regular is highly imbalanced (91.9%)Imbalance
Minimum_kms_to_be_covered_in_a_day is highly imbalanced (67.1%)Imbalance
customerID is highly imbalanced (54.2%)Imbalance
customerNameCode is highly imbalanced (53.9%)Imbalance
GpsProvider has 953 (13.9%) missing valuesMissing
Data_Ping_time has 953 (13.9%) missing valuesMissing
Current_Location has 964 (14.0%) missing valuesMissing
Curr_lat has 953 (13.9%) missing valuesMissing
Curr_lon has 953 (13.9%) missing valuesMissing
ontime has 4332 (63.0%) missing valuesMissing
delay has 2538 (36.9%) missing valuesMissing
trip_end_date has 194 (2.8%) missing valuesMissing
TRANSPORTATION_DISTANCE_IN_KM has 712 (10.3%) missing valuesMissing
vehicleType has 828 (12.0%) missing valuesMissing
Minimum_kms_to_be_covered_in_a_day has 4060 (59.0%) missing valuesMissing
Driver_Name has 3429 (49.8%) missing valuesMissing
Driver_MobileNo has 4189 (60.9%) missing valuesMissing
Planned_ETA is an unsupported type, check if it needs cleaning or further analysisUnsupported
actual_eta is an unsupported type, check if it needs cleaning or further analysisUnsupported
OriginLocation_Code is an unsupported type, check if it needs cleaning or further analysisUnsupported
DestinationLocation_Code is an unsupported type, check if it needs cleaning or further analysisUnsupported
trip_start_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
trip_end_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
supplierID is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-08-12 22:37:03.834123
Analysis finished2024-08-12 22:37:12.792083
Duration8.96 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

GpsProvider
Categorical

IMBALANCE  MISSING 

Distinct29
Distinct (%)0.5%
Missing953
Missing (%)13.9%
Memory size53.9 KiB
CONSENT TRACK
3859 
VAMOSYS
788 
EKTA
 
322
JTECH
 
241
MANUAL
 
219
Other values (24)
498 

Length

Max length22
Median length13
Mean length10.914122
Min length4

Characters and Unicode

Total characters64688
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCONSENT TRACK
2nd rowVAMOSYS
3rd rowCONSENT TRACK
4th rowVAMOSYS
5th rowVAMOSYS

Common Values

ValueCountFrequency (%)
CONSENT TRACK 3859
56.1%
VAMOSYS 788
 
11.5%
EKTA 322
 
4.7%
JTECH 241
 
3.5%
MANUAL 219
 
3.2%
KRC LOGISTICS 175
 
2.5%
WDSL 72
 
1.0%
BEECON 34
 
0.5%
NUEVASTECH 29
 
0.4%
Garuda 28
 
0.4%
Other values (19) 160
 
2.3%
(Missing) 953
 
13.9%

Length

2024-08-13T04:07:13.173153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
consent 3859
38.5%
track 3859
38.5%
vamosys 788
 
7.9%
ekta 322
 
3.2%
jtech 241
 
2.4%
manual 219
 
2.2%
logistics 188
 
1.9%
krc 175
 
1.7%
wdsl 72
 
0.7%
beecon 34
 
0.3%
Other values (25) 260
 
2.6%

Most occurring characters

ValueCountFrequency (%)
T 8639
13.4%
C 8488
13.1%
N 8079
12.5%
S 6069
9.4%
A 5629
8.7%
O 4964
7.7%
E 4639
7.2%
K 4369
6.8%
R 4132
6.4%
4090
6.3%
Other values (32) 5590
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 8639
13.4%
C 8488
13.1%
N 8079
12.5%
S 6069
9.4%
A 5629
8.7%
O 4964
7.7%
E 4639
7.2%
K 4369
6.8%
R 4132
6.4%
4090
6.3%
Other values (32) 5590
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 8639
13.4%
C 8488
13.1%
N 8079
12.5%
S 6069
9.4%
A 5629
8.7%
O 4964
7.7%
E 4639
7.2%
K 4369
6.8%
R 4132
6.4%
4090
6.3%
Other values (32) 5590
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 8639
13.4%
C 8488
13.1%
N 8079
12.5%
S 6069
9.4%
A 5629
8.7%
O 4964
7.7%
E 4639
7.2%
K 4369
6.8%
R 4132
6.4%
4090
6.3%
Other values (32) 5590
8.6%
Distinct6875
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:13.572595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length12.722093
Min length12

Characters and Unicode

Total characters87528
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6871 ?
Unique (%)99.9%

Sample

1st rowMVCV0000927/082021
2nd rowVCV00014271/082021
3rd rowVCV00014382/082021
4th rowVCV00014743/082021
5th rowVCV00014744/082021
ValueCountFrequency (%)
mvcv0000798/082021 3
 
< 0.1%
mvcv0000759/082021 2
 
< 0.1%
vcv00014072/082021 2
 
< 0.1%
aeibk1902026 2
 
< 0.1%
vcv00014554/082021 1
 
< 0.1%
vcv00014743/082021 1
 
< 0.1%
vcv00014744/082021 1
 
< 0.1%
vcv00014749/082021 1
 
< 0.1%
vcv00014750/082021 1
 
< 0.1%
vcv00014812/082021 1
 
< 0.1%
Other values (6865) 6865
99.8%
2024-08-13T04:07:14.302515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11595
13.2%
2 9561
 
10.9%
1 6531
 
7.5%
K 6052
 
6.9%
B 6052
 
6.9%
E 4511
 
5.2%
A 4511
 
5.2%
I 4511
 
5.2%
9 4121
 
4.7%
4 3984
 
4.6%
Other values (14) 26099
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11595
13.2%
2 9561
 
10.9%
1 6531
 
7.5%
K 6052
 
6.9%
B 6052
 
6.9%
E 4511
 
5.2%
A 4511
 
5.2%
I 4511
 
5.2%
9 4121
 
4.7%
4 3984
 
4.6%
Other values (14) 26099
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11595
13.2%
2 9561
 
10.9%
1 6531
 
7.5%
K 6052
 
6.9%
B 6052
 
6.9%
E 4511
 
5.2%
A 4511
 
5.2%
I 4511
 
5.2%
9 4121
 
4.7%
4 3984
 
4.6%
Other values (14) 26099
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11595
13.2%
2 9561
 
10.9%
1 6531
 
7.5%
K 6052
 
6.9%
B 6052
 
6.9%
E 4511
 
5.2%
A 4511
 
5.2%
I 4511
 
5.2%
9 4121
 
4.7%
4 3984
 
4.6%
Other values (14) 26099
29.8%

Market/Regular
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
Regular
6811 
Market
 
69

Length

Max length7
Median length7
Mean length6.9899709
Min length6

Characters and Unicode

Total characters48091
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarket
2nd rowRegular
3rd rowRegular
4th rowRegular
5th rowRegular

Common Values

ValueCountFrequency (%)
Regular 6811
99.0%
Market 69
 
1.0%

Length

2024-08-13T04:07:14.578859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T04:07:14.869426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
regular 6811
99.0%
market 69
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 6880
14.3%
a 6880
14.3%
r 6880
14.3%
R 6811
14.2%
g 6811
14.2%
u 6811
14.2%
l 6811
14.2%
M 69
 
0.1%
k 69
 
0.1%
t 69
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48091
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6880
14.3%
a 6880
14.3%
r 6880
14.3%
R 6811
14.2%
g 6811
14.2%
u 6811
14.2%
l 6811
14.2%
M 69
 
0.1%
k 69
 
0.1%
t 69
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48091
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6880
14.3%
a 6880
14.3%
r 6880
14.3%
R 6811
14.2%
g 6811
14.2%
u 6811
14.2%
l 6811
14.2%
M 69
 
0.1%
k 69
 
0.1%
t 69
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48091
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6880
14.3%
a 6880
14.3%
r 6880
14.3%
R 6811
14.2%
g 6811
14.2%
u 6811
14.2%
l 6811
14.2%
M 69
 
0.1%
k 69
 
0.1%
t 69
 
0.1%
Distinct6005
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
Minimum2019-03-18 12:19:22
Maximum2020-12-03 13:10:21
2024-08-13T04:07:15.086327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:15.407414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2325
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:15.921747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.4103198
Min length7

Characters and Unicode

Total characters64743
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1381 ?
Unique (%)20.1%

Sample

1st rowKA590408
2nd rowTN30BC5917
3rd rowTN22AR2748
4th rowTN28AQ0781
5th rowTN68F1722
ValueCountFrequency (%)
ts15uc9341 37
 
0.5%
ka01af0114 31
 
0.4%
ka21a5090 31
 
0.4%
ka21a3643 30
 
0.4%
ts15ud3698 29
 
0.4%
ka029324 28
 
0.4%
tn04ac4746 27
 
0.4%
ka219934 27
 
0.4%
ts15ud5174 26
 
0.4%
ts15ud3697 26
 
0.4%
Other values (2337) 6624
95.8%
2024-08-13T04:07:16.489308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6294
 
9.7%
0 4759
 
7.4%
4 4118
 
6.4%
3 4052
 
6.3%
5 4049
 
6.3%
2 3811
 
5.9%
8 3740
 
5.8%
9 3582
 
5.5%
7 3524
 
5.4%
6 3343
 
5.2%
Other values (36) 23471
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6294
 
9.7%
0 4759
 
7.4%
4 4118
 
6.4%
3 4052
 
6.3%
5 4049
 
6.3%
2 3811
 
5.9%
8 3740
 
5.8%
9 3582
 
5.5%
7 3524
 
5.4%
6 3343
 
5.2%
Other values (36) 23471
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6294
 
9.7%
0 4759
 
7.4%
4 4118
 
6.4%
3 4052
 
6.3%
5 4049
 
6.3%
2 3811
 
5.9%
8 3740
 
5.8%
9 3582
 
5.5%
7 3524
 
5.4%
6 3343
 
5.2%
Other values (36) 23471
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6294
 
9.7%
0 4759
 
7.4%
4 4118
 
6.4%
3 4052
 
6.3%
5 4049
 
6.3%
2 3811
 
5.9%
8 3740
 
5.8%
9 3582
 
5.5%
7 3524
 
5.4%
6 3343
 
5.2%
Other values (36) 23471
36.3%
Distinct180
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:16.912859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length56
Median length46
Mean length33.112355
Min length16

Characters and Unicode

Total characters227813
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)0.6%

Sample

1st rowTVSLSL-PUZHAL-HUB,CHENNAI,TAMIL NADU
2nd rowDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU
3rd rowLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY
4th rowDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU
5th rowLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY
ValueCountFrequency (%)
nadu 1669
 
6.4%
tamil 1181
 
4.5%
karnataka 931
 
3.6%
bangalore 927
 
3.6%
india 877
 
3.4%
maharashtra 701
 
2.7%
mugabala 572
 
2.2%
rural 572
 
2.2%
kanchipuram 557
 
2.1%
gujarat 476
 
1.8%
Other values (324) 17537
67.5%
2024-08-13T04:07:17.598223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35802
 
15.7%
19120
 
8.4%
, 13349
 
5.9%
r 13281
 
5.8%
n 8867
 
3.9%
l 8748
 
3.8%
u 7961
 
3.5%
i 7801
 
3.4%
e 7344
 
3.2%
h 7316
 
3.2%
Other values (51) 98224
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 227813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 35802
 
15.7%
19120
 
8.4%
, 13349
 
5.9%
r 13281
 
5.8%
n 8867
 
3.9%
l 8748
 
3.8%
u 7961
 
3.5%
i 7801
 
3.4%
e 7344
 
3.2%
h 7316
 
3.2%
Other values (51) 98224
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 227813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 35802
 
15.7%
19120
 
8.4%
, 13349
 
5.9%
r 13281
 
5.8%
n 8867
 
3.9%
l 8748
 
3.8%
u 7961
 
3.5%
i 7801
 
3.4%
e 7344
 
3.2%
h 7316
 
3.2%
Other values (51) 98224
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 227813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 35802
 
15.7%
19120
 
8.4%
, 13349
 
5.9%
r 13281
 
5.8%
n 8867
 
3.9%
l 8748
 
3.8%
u 7961
 
3.5%
i 7801
 
3.4%
e 7344
 
3.2%
h 7316
 
3.2%
Other values (51) 98224
43.1%
Distinct520
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:17.989259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length58
Median length49
Mean length34.14157
Min length10

Characters and Unicode

Total characters234894
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)2.5%

Sample

1st rowASHOK LEYLAND PLANT 1- HOSUR,HOSUR,KARNATAKA
2nd rowDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU
3rd rowLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY
4th rowDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU
5th rowLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY
ValueCountFrequency (%)
nadu 1719
 
6.5%
tamil 1238
 
4.7%
bangalore 1047
 
3.9%
india 878
 
3.3%
karnataka 857
 
3.2%
kanchipuram 648
 
2.4%
west 595
 
2.2%
gujarat 499
 
1.9%
haryana 496
 
1.9%
delhi 462
 
1.7%
Other values (834) 18073
68.2%
2024-08-13T04:07:18.617345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35110
 
14.9%
19632
 
8.4%
r 13936
 
5.9%
, 13768
 
5.9%
n 11253
 
4.8%
i 9260
 
3.9%
l 7698
 
3.3%
h 7694
 
3.3%
e 7394
 
3.1%
d 7373
 
3.1%
Other values (55) 101776
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 35110
 
14.9%
19632
 
8.4%
r 13936
 
5.9%
, 13768
 
5.9%
n 11253
 
4.8%
i 9260
 
3.9%
l 7698
 
3.3%
h 7694
 
3.3%
e 7394
 
3.1%
d 7373
 
3.1%
Other values (55) 101776
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 35110
 
14.9%
19632
 
8.4%
r 13936
 
5.9%
, 13768
 
5.9%
n 11253
 
4.8%
i 9260
 
3.9%
l 7698
 
3.3%
h 7694
 
3.3%
e 7394
 
3.1%
d 7373
 
3.1%
Other values (55) 101776
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 35110
 
14.9%
19632
 
8.4%
r 13936
 
5.9%
, 13768
 
5.9%
n 11253
 
4.8%
i 9260
 
3.9%
l 7698
 
3.3%
h 7694
 
3.3%
e 7394
 
3.1%
d 7373
 
3.1%
Other values (55) 101776
43.3%
Distinct173
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:19.258125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length37
Median length36
Mean length25.484448
Min length15

Characters and Unicode

Total characters175333
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)0.6%

Sample

1st row13.1550,80.1960
2nd row12.8390,79.9540
3rd row11.8710,79.7390
4th row12.8390,79.9540
5th row11.8720,79.6320
ValueCountFrequency (%)
16.560192249175344,80.792293091599547 1189
 
17.3%
18.750621,73.87719 428
 
6.2%
12.0001,79.74839949999999 409
 
5.9%
26.192290403509681,91.751276775362513 380
 
5.5%
12.8390,79.9540 342
 
5.0%
28.373519,76.835337 254
 
3.7%
22.961777,72.094219 217
 
3.2%
23.525267916088961,87.264424348570884 214
 
3.1%
13.007503209603689,77.665098855934886 199
 
2.9%
12.992699792116747,77.803841411108593 161
 
2.3%
Other values (163) 3087
44.9%
2024-08-13T04:07:20.113815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 22674
12.9%
7 19682
11.2%
1 18676
10.7%
2 17607
10.0%
. 13760
7.8%
0 13673
7.8%
8 13167
7.5%
5 13038
7.4%
3 12428
7.1%
6 12024
6.9%
Other values (2) 18604
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 175333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 22674
12.9%
7 19682
11.2%
1 18676
10.7%
2 17607
10.0%
. 13760
7.8%
0 13673
7.8%
8 13167
7.5%
5 13038
7.4%
3 12428
7.1%
6 12024
6.9%
Other values (2) 18604
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 175333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 22674
12.9%
7 19682
11.2%
1 18676
10.7%
2 17607
10.0%
. 13760
7.8%
0 13673
7.8%
8 13167
7.5%
5 13038
7.4%
3 12428
7.1%
6 12024
6.9%
Other values (2) 18604
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 175333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 22674
12.9%
7 19682
11.2%
1 18676
10.7%
2 17607
10.0%
. 13760
7.8%
0 13673
7.8%
8 13167
7.5%
5 13038
7.4%
3 12428
7.1%
6 12024
6.9%
Other values (2) 18604
10.6%
Distinct522
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:20.522510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length37
Median length36
Mean length22.238663
Min length14

Characters and Unicode

Total characters153002
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)2.6%

Sample

1st row12.7400,77.8200
2nd row12.8390,79.9540
3rd row11.8710,79.7390
4th row12.8390,79.9540
5th row11.8720,79.6320
ValueCountFrequency (%)
12.8390,79.9540 342
 
5.0%
12.786517,79.975221 252
 
3.7%
13.199089183304451,77.708554234959038 227
 
3.3%
12.930429,79.931163 209
 
3.0%
22.961777,72.094219 182
 
2.6%
18.750621,73.87719 180
 
2.6%
28.373519,76.835337 171
 
2.5%
22.95210237429815,88.457014796076109 124
 
1.8%
12.843107191355518,77.5942828776749 123
 
1.8%
12.777874729699617,77.642275537347089 114
 
1.7%
Other values (512) 4956
72.0%
2024-08-13T04:07:21.248406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 19782
12.9%
2 16536
10.8%
9 14877
9.7%
1 14763
9.6%
. 13760
9.0%
8 13191
8.6%
3 11759
7.7%
0 10936
7.1%
6 10426
6.8%
5 10093
6.6%
Other values (2) 16879
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 19782
12.9%
2 16536
10.8%
9 14877
9.7%
1 14763
9.6%
. 13760
9.0%
8 13191
8.6%
3 11759
7.7%
0 10936
7.1%
6 10426
6.8%
5 10093
6.6%
Other values (2) 16879
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 19782
12.9%
2 16536
10.8%
9 14877
9.7%
1 14763
9.6%
. 13760
9.0%
8 13191
8.6%
3 11759
7.7%
0 10936
7.1%
6 10426
6.8%
5 10093
6.6%
Other values (2) 16879
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 19782
12.9%
2 16536
10.8%
9 14877
9.7%
1 14763
9.6%
. 13760
9.0%
8 13191
8.6%
3 11759
7.7%
0 10936
7.1%
6 10426
6.8%
5 10093
6.6%
Other values (2) 16879
11.0%

Data_Ping_time
Date

MISSING 

Distinct3756
Distinct (%)63.4%
Missing953
Missing (%)13.9%
Memory size53.9 KiB
Minimum2019-06-07 18:25:10
Maximum2020-08-28 12:40:31
2024-08-13T04:07:21.488273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:22.091807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Planned_ETA
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size53.9 KiB

Current_Location
Text

MISSING 

Distinct2567
Distinct (%)43.4%
Missing964
Missing (%)14.0%
Memory size53.9 KiB
2024-08-13T04:07:22.514142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length241
Median length138
Mean length67.408384
Min length11

Characters and Unicode

Total characters398788
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1745 ?
Unique (%)29.5%

Sample

1st rowVaniyambadi Rd, Valayambattu, Tamil Nadu 635752, India
2nd rowUnnamed Road, Oragadam Industrial Corridor, Vattambakkam R.F., Tamil Nadu 631605, India
3rd row570, National Hwy 48, Shenoy Nagar, Chennai, Tamil Nadu 600030, India
4th rowSingaperumal Koil - Sriperumbudur Rd, Oragadam Industrial Corridor, Vattambakkam R.F., Tamil Nadu 631605, India
5th rowMelmaruvathur, Tamil Nadu 603319, India
ValueCountFrequency (%)
india 5927
 
10.7%
rd 1843
 
3.3%
road 1786
 
3.2%
tamil 1614
 
2.9%
nadu 1614
 
2.9%
unnamed 1455
 
2.6%
933
 
1.7%
nagar 867
 
1.6%
karnataka 787
 
1.4%
industrial 782
 
1.4%
Other values (5625) 37564
68.1%
2024-08-13T04:07:23.340531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 53279
 
13.4%
49256
 
12.4%
, 24109
 
6.0%
n 20265
 
5.1%
d 19957
 
5.0%
i 19051
 
4.8%
r 17828
 
4.5%
l 11543
 
2.9%
e 11328
 
2.8%
t 10720
 
2.7%
Other values (69) 161452
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 398788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 53279
 
13.4%
49256
 
12.4%
, 24109
 
6.0%
n 20265
 
5.1%
d 19957
 
5.0%
i 19051
 
4.8%
r 17828
 
4.5%
l 11543
 
2.9%
e 11328
 
2.8%
t 10720
 
2.7%
Other values (69) 161452
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 398788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 53279
 
13.4%
49256
 
12.4%
, 24109
 
6.0%
n 20265
 
5.1%
d 19957
 
5.0%
i 19051
 
4.8%
r 17828
 
4.5%
l 11543
 
2.9%
e 11328
 
2.8%
t 10720
 
2.7%
Other values (69) 161452
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 398788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 53279
 
13.4%
49256
 
12.4%
, 24109
 
6.0%
n 20265
 
5.1%
d 19957
 
5.0%
i 19051
 
4.8%
r 17828
 
4.5%
l 11543
 
2.9%
e 11328
 
2.8%
t 10720
 
2.7%
Other values (69) 161452
40.5%
Distinct520
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:23.776678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length58
Median length49
Mean length34.14157
Min length10

Characters and Unicode

Total characters234894
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)2.5%

Sample

1st rowASHOK LEYLAND PLANT 1- HOSUR,HOSUR,KARNATAKA
2nd rowDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU
3rd rowLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY
4th rowDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU
5th rowLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY
ValueCountFrequency (%)
nadu 1719
 
6.5%
tamil 1238
 
4.7%
bangalore 1047
 
3.9%
india 878
 
3.3%
karnataka 857
 
3.2%
kanchipuram 648
 
2.4%
west 595
 
2.2%
gujarat 499
 
1.9%
haryana 496
 
1.9%
delhi 462
 
1.7%
Other values (834) 18073
68.2%
2024-08-13T04:07:24.402222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35110
 
14.9%
19632
 
8.4%
r 13936
 
5.9%
, 13768
 
5.9%
n 11253
 
4.8%
i 9260
 
3.9%
l 7698
 
3.3%
h 7694
 
3.3%
e 7394
 
3.1%
d 7373
 
3.1%
Other values (55) 101776
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 35110
 
14.9%
19632
 
8.4%
r 13936
 
5.9%
, 13768
 
5.9%
n 11253
 
4.8%
i 9260
 
3.9%
l 7698
 
3.3%
h 7694
 
3.3%
e 7394
 
3.1%
d 7373
 
3.1%
Other values (55) 101776
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 35110
 
14.9%
19632
 
8.4%
r 13936
 
5.9%
, 13768
 
5.9%
n 11253
 
4.8%
i 9260
 
3.9%
l 7698
 
3.3%
h 7694
 
3.3%
e 7394
 
3.1%
d 7373
 
3.1%
Other values (55) 101776
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 35110
 
14.9%
19632
 
8.4%
r 13936
 
5.9%
, 13768
 
5.9%
n 11253
 
4.8%
i 9260
 
3.9%
l 7698
 
3.3%
h 7694
 
3.3%
e 7394
 
3.1%
d 7373
 
3.1%
Other values (55) 101776
43.3%

actual_eta
Unsupported

REJECTED  UNSUPPORTED 

Missing37
Missing (%)0.5%
Memory size53.9 KiB

Curr_lat
Real number (ℝ)

MISSING 

Distinct4139
Distinct (%)69.8%
Missing953
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean18.679995
Minimum8.16679
Maximum32.367928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:24.589384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8.16679
5-th percentile12.485942
Q112.871868
median17.470922
Q323.202009
95-th percentile28.536316
Maximum32.367928
Range24.201138
Interquartile range (IQR)10.330141

Descriptive statistics

Standard deviation6.0755609
Coefficient of variation (CV)0.32524425
Kurtosis-1.3083293
Mean18.679995
Median Absolute Deviation (MAD)4.6892575
Skewness0.40896195
Sum110716.33
Variance36.91244
MonotonicityNot monotonic
2024-08-13T04:07:24.863676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.46342 33
 
0.5%
17.4709225 21
 
0.3%
19.032803 20
 
0.3%
13.166807 18
 
0.3%
13.19702 16
 
0.2%
28.20895 16
 
0.2%
12.781673 15
 
0.2%
13.1191355 14
 
0.2%
13.199137 13
 
0.2%
28.52547 13
 
0.2%
Other values (4129) 5748
83.5%
(Missing) 953
 
13.9%
ValueCountFrequency (%)
8.16679 1
< 0.1%
8.194479 1
< 0.1%
8.70089 1
< 0.1%
8.7388 1
< 0.1%
8.784697 1
< 0.1%
8.81781 1
< 0.1%
8.912319 1
< 0.1%
9.061639444 1
< 0.1%
9.369471 1
< 0.1%
9.483545 2
< 0.1%
ValueCountFrequency (%)
32.367928 1
< 0.1%
32.3675 1
< 0.1%
31.360373 1
< 0.1%
31.360283 1
< 0.1%
31.35795222 1
< 0.1%
31.34324 1
< 0.1%
31.337187 1
< 0.1%
31.33258 1
< 0.1%
31.33047583 1
< 0.1%
31.12698194 1
< 0.1%

Curr_lon
Real number (ℝ)

MISSING 

Distinct4109
Distinct (%)69.3%
Missing953
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean78.759745
Minimum69.657698
Maximum95.52955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:25.074628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum69.657698
5-th percentile72.414279
Q176.891857
median77.84334
Q380.018999
95-th percentile88.27194
Maximum95.52955
Range25.871852
Interquartile range (IQR)3.127142

Descriptive statistics

Standard deviation4.2163571
Coefficient of variation (CV)0.053534417
Kurtosis1.2433644
Mean78.759745
Median Absolute Deviation (MAD)2.1070778
Skewness0.96375247
Sum466809.01
Variance17.777667
MonotonicityNot monotonic
2024-08-13T04:07:25.366715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.19776 33
 
0.5%
78.22128306 21
 
0.3%
73.1012153 20
 
0.3%
80.29405 18
 
0.3%
77.71069 16
 
0.2%
77.30532 16
 
0.2%
80.01655 15
 
0.2%
77.8888891 14
 
0.2%
77.28333 13
 
0.2%
77.711136 13
 
0.2%
Other values (4099) 5748
83.5%
(Missing) 953
 
13.9%
ValueCountFrequency (%)
69.65769778 1
 
< 0.1%
69.8644 1
 
< 0.1%
70.06671 1
 
< 0.1%
70.098045 1
 
< 0.1%
70.11327 1
 
< 0.1%
70.5046 1
 
< 0.1%
70.83222639 1
 
< 0.1%
71.45707361 1
 
< 0.1%
71.97328083 1
 
< 0.1%
71.97397667 5
0.1%
ValueCountFrequency (%)
95.52955 1
< 0.1%
94.94118694 1
< 0.1%
94.91118917 1
< 0.1%
94.88311 1
< 0.1%
94.45472 1
< 0.1%
94.20018 1
< 0.1%
94.19761417 1
< 0.1%
93.74356972 1
< 0.1%
92.79803 1
< 0.1%
92.79426111 1
< 0.1%

ontime
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4332
Missing (%)63.0%
Memory size53.9 KiB
G
2548 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2548
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowG
5th rowG

Common Values

ValueCountFrequency (%)
G 2548
37.0%
(Missing) 4332
63.0%

Length

2024-08-13T04:07:25.620849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T04:07:25.931207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
g 2548
100.0%

Most occurring characters

ValueCountFrequency (%)
G 2548
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 2548
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 2548
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 2548
100.0%

delay
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing2538
Missing (%)36.9%
Memory size53.9 KiB
R
4342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4342
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 4342
63.1%
(Missing) 2538
36.9%

Length

2024-08-13T04:07:26.081631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T04:07:26.342005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
r 4342
100.0%

Most occurring characters

ValueCountFrequency (%)
R 4342
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 4342
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 4342
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 4342
100.0%

OriginLocation_Code
Unsupported

REJECTED  UNSUPPORTED 

Missing3
Missing (%)< 0.1%
Memory size53.9 KiB

DestinationLocation_Code
Unsupported

REJECTED  UNSUPPORTED 

Missing27
Missing (%)0.4%
Memory size53.9 KiB

trip_start_date
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size53.9 KiB

trip_end_date
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing194
Missing (%)2.8%
Memory size53.9 KiB

TRANSPORTATION_DISTANCE_IN_KM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct564
Distinct (%)9.1%
Missing712
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean553.85628
Minimum0
Maximum2954.7
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:26.517706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q140
median160
Q3900
95-th percentile2400
Maximum2954.7
Range2954.7
Interquartile range (IQR)860

Descriptive statistics

Standard deviation758.98184
Coefficient of variation (CV)1.3703588
Kurtosis0.8988935
Mean553.85628
Median Absolute Deviation (MAD)135
Skewness1.4730875
Sum3416185.5
Variance576053.43
MonotonicityNot monotonic
2024-08-13T04:07:26.824136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 384
 
5.6%
30 227
 
3.3%
1900 201
 
2.9%
40 180
 
2.6%
70 177
 
2.6%
49 143
 
2.1%
1290 137
 
2.0%
2425 132
 
1.9%
31 122
 
1.8%
1199 121
 
1.8%
Other values (554) 4344
63.1%
(Missing) 712
 
10.3%
ValueCountFrequency (%)
0 18
 
0.3%
3 46
0.7%
5 2
 
< 0.1%
7.6 5
 
0.1%
8.2 3
 
< 0.1%
9.4 16
 
0.2%
10 1
 
< 0.1%
10.2 2
 
< 0.1%
12 3
 
< 0.1%
12.8 1
 
< 0.1%
ValueCountFrequency (%)
2954.7 1
 
< 0.1%
2898 1
 
< 0.1%
2884 1
 
< 0.1%
2800 2
 
< 0.1%
2750 29
0.4%
2723 2
 
< 0.1%
2710 1
 
< 0.1%
2704 1
 
< 0.1%
2700 33
0.5%
2681 1
 
< 0.1%

vehicleType
Categorical

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)0.7%
Missing828
Missing (%)12.0%
Memory size53.9 KiB
40 FT 3XL Trailer 35MT
2575 
32 FT Multi-Axle 14MT - HCV
957 
40 FT Flat Bed Multi-Axle 27MT - Trailer
670 
32 FT Single-Axle 7MT - HCV
491 
28 FT Open Body 25MT
 
171
Other values (39)
1188 

Length

Max length47
Median length41
Mean length26.392102
Min length15

Characters and Unicode

Total characters159725
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row32 FT Single-Axle 7MT - HCV
2nd row32 FT Multi-Axle 14MT - HCV
3rd row1 MT Tata Ace (Open Body)
4th row32 FT Multi-Axle 14MT - HCV
5th row32 FT Multi-Axle 14MT - HCV

Common Values

ValueCountFrequency (%)
40 FT 3XL Trailer 35MT 2575
37.4%
32 FT Multi-Axle 14MT - HCV 957
 
13.9%
40 FT Flat Bed Multi-Axle 27MT - Trailer 670
 
9.7%
32 FT Single-Axle 7MT - HCV 491
 
7.1%
28 FT Open Body 25MT 171
 
2.5%
24 / 26 FT Taurus Open 21MT - HCV 158
 
2.3%
24 | 26 FT Taurus Open 21MT - HCV 157
 
2.3%
40 FT Flat Bed Double-Axle 21MT - Trailer 152
 
2.2%
22 FT Taurus Open 16MT - HCV 133
 
1.9%
19 FT Open 7MT - MCV 111
 
1.6%
Other values (34) 477
 
6.9%
(Missing) 828
 
12.0%

Length

2024-08-13T04:07:27.133844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ft 5977
16.7%
trailer 3404
 
9.5%
40 3401
 
9.5%
3244
 
9.0%
3xl 2575
 
7.2%
35mt 2575
 
7.2%
hcv 1933
 
5.4%
multi-axle 1716
 
4.8%
32 1543
 
4.3%
14mt 957
 
2.7%
Other values (60) 8570
23.9%

Most occurring characters

ValueCountFrequency (%)
29880
18.7%
T 15827
 
9.9%
l 9045
 
5.7%
e 8377
 
5.2%
M 7940
 
5.0%
r 7416
 
4.6%
F 6804
 
4.3%
3 6727
 
4.2%
i 5862
 
3.7%
- 5367
 
3.4%
Other values (43) 56480
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29880
18.7%
T 15827
 
9.9%
l 9045
 
5.7%
e 8377
 
5.2%
M 7940
 
5.0%
r 7416
 
4.6%
F 6804
 
4.3%
3 6727
 
4.2%
i 5862
 
3.7%
- 5367
 
3.4%
Other values (43) 56480
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29880
18.7%
T 15827
 
9.9%
l 9045
 
5.7%
e 8377
 
5.2%
M 7940
 
5.0%
r 7416
 
4.6%
F 6804
 
4.3%
3 6727
 
4.2%
i 5862
 
3.7%
- 5367
 
3.4%
Other values (43) 56480
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29880
18.7%
T 15827
 
9.9%
l 9045
 
5.7%
e 8377
 
5.2%
M 7940
 
5.0%
r 7416
 
4.6%
F 6804
 
4.3%
3 6727
 
4.2%
i 5862
 
3.7%
- 5367
 
3.4%
Other values (43) 56480
35.4%

Minimum_kms_to_be_covered_in_a_day
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.1%
Missing4060
Missing (%)59.0%
Memory size53.9 KiB
250.0
2529 
275.0
267 
0.0
 
24

Length

Max length5
Median length5
Mean length4.9829787
Min length3

Characters and Unicode

Total characters14052
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row250.0
2nd row250.0
3rd row250.0
4th row250.0
5th row250.0

Common Values

ValueCountFrequency (%)
250.0 2529
36.8%
275.0 267
 
3.9%
0.0 24
 
0.3%
(Missing) 4060
59.0%

Length

2024-08-13T04:07:27.421949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T04:07:27.697157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
250.0 2529
89.7%
275.0 267
 
9.5%
0.0 24
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 5373
38.2%
. 2820
20.1%
2 2796
19.9%
5 2796
19.9%
7 267
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5373
38.2%
. 2820
20.1%
2 2796
19.9%
5 2796
19.9%
7 267
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5373
38.2%
. 2820
20.1%
2 2796
19.9%
5 2796
19.9%
7 267
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5373
38.2%
. 2820
20.1%
2 2796
19.9%
5 2796
19.9%
7 267
 
1.9%

Driver_Name
Text

MISSING 

Distinct1355
Distinct (%)39.3%
Missing3429
Missing (%)49.8%
Memory size53.9 KiB
2024-08-13T04:07:28.218344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length45
Median length20
Mean length10.064039
Min length2

Characters and Unicode

Total characters34731
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique800 ?
Unique (%)23.2%

Sample

1st rowRAMESH
2nd rowGIRI
3rd rowRAVI
4th rowTAMIL
5th rowGANESH
ValueCountFrequency (%)
kumar 353
 
6.0%
singh 175
 
3.0%
khan 169
 
2.9%
k 83
 
1.4%
yadav 76
 
1.3%
s 66
 
1.1%
manu 64
 
1.1%
n 63
 
1.1%
r 59
 
1.0%
m 58
 
1.0%
Other values (1285) 4712
80.2%
2024-08-13T04:07:29.079190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5204
 
15.0%
2485
 
7.2%
R 2116
 
6.1%
N 2021
 
5.8%
S 1781
 
5.1%
M 1684
 
4.8%
H 1601
 
4.6%
I 1546
 
4.5%
a 1443
 
4.2%
K 1432
 
4.1%
Other values (45) 13418
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5204
 
15.0%
2485
 
7.2%
R 2116
 
6.1%
N 2021
 
5.8%
S 1781
 
5.1%
M 1684
 
4.8%
H 1601
 
4.6%
I 1546
 
4.5%
a 1443
 
4.2%
K 1432
 
4.1%
Other values (45) 13418
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5204
 
15.0%
2485
 
7.2%
R 2116
 
6.1%
N 2021
 
5.8%
S 1781
 
5.1%
M 1684
 
4.8%
H 1601
 
4.6%
I 1546
 
4.5%
a 1443
 
4.2%
K 1432
 
4.1%
Other values (45) 13418
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5204
 
15.0%
2485
 
7.2%
R 2116
 
6.1%
N 2021
 
5.8%
S 1781
 
5.1%
M 1684
 
4.8%
H 1601
 
4.6%
I 1546
 
4.5%
a 1443
 
4.2%
K 1432
 
4.1%
Other values (45) 13418
38.6%

Driver_MobileNo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1273
Distinct (%)47.3%
Missing4189
Missing (%)60.9%
Infinite0
Infinite (%)0.0%
Mean8.5989813 × 109
Minimum6.0005463 × 109
Maximum1 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:29.369273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.0005463 × 109
5-th percentile6.3837783 × 109
Q17.6515046 × 109
median8.9307619 × 109
Q39.6349814 × 109
95-th percentile9.9521734 × 109
Maximum1 × 1010
Range3.9994537 × 109
Interquartile range (IQR)1.9834768 × 109

Descriptive statistics

Standard deviation1.1316687 × 109
Coefficient of variation (CV)0.13160498
Kurtosis-1.019947
Mean8.5989813 × 109
Median Absolute Deviation (MAD)8.6022544 × 108
Skewness-0.5050876
Sum2.3139859 × 1013
Variance1.2806742 × 1018
MonotonicityNot monotonic
2024-08-13T04:07:29.670247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9952349318 28
 
0.4%
7868857313 18
 
0.3%
9600691019 18
 
0.3%
7082423070 18
 
0.3%
7082423058 17
 
0.2%
9944446299 17
 
0.2%
9952173355 16
 
0.2%
9688429909 14
 
0.2%
8015761413 14
 
0.2%
8295883296 14
 
0.2%
Other values (1263) 2517
36.6%
(Missing) 4189
60.9%
ValueCountFrequency (%)
6000546262 3
< 0.1%
6006291329 1
 
< 0.1%
6006450076 2
< 0.1%
6201157407 2
< 0.1%
6201982709 1
 
< 0.1%
6201994678 2
< 0.1%
6202125568 2
< 0.1%
6203975611 2
< 0.1%
6204355364 1
 
< 0.1%
6204621632 1
 
< 0.1%
ValueCountFrequency (%)
9999999999 7
0.1%
9999681295 3
< 0.1%
9999031538 2
 
< 0.1%
9998843103 1
 
< 0.1%
9997623277 1
 
< 0.1%
9997471510 1
 
< 0.1%
9997295833 2
 
< 0.1%
9996036863 1
 
< 0.1%
9994900412 1
 
< 0.1%
9994798323 2
 
< 0.1%

customerID
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct39
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
LTLEXMUM40
4092 
FILEXCHE19
657 
DMREXCHEUX
588 
ALLEXCHE45
 
251
LUTGCCHE06
 
222
Other values (34)
1070 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters68800
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowALLEXCHE45
2nd rowDMREXCHEUX
3rd rowLUTGCCHE06
4th rowDMREXCHEUX
5th rowLUTGCCHE06

Common Values

ValueCountFrequency (%)
LTLEXMUM40 4092
59.5%
FILEXCHE19 657
 
9.5%
DMREXCHEUX 588
 
8.5%
ALLEXCHE45 251
 
3.6%
LUTGCCHE06 222
 
3.2%
ERCEXPUNTG 211
 
3.1%
BILGCCHE02 161
 
2.3%
TMCGCCHE05 117
 
1.7%
OTIEXHOSJM 108
 
1.6%
WENEXBAN18 78
 
1.1%
Other values (29) 395
 
5.7%

Length

2024-08-13T04:07:30.026740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ltlexmum40 4092
59.5%
filexche19 657
 
9.5%
dmrexcheux 588
 
8.5%
allexche45 251
 
3.6%
lutgcche06 222
 
3.2%
ercexpuntg 211
 
3.1%
bilgcche02 161
 
2.3%
tmcgcche05 117
 
1.7%
otiexhosjm 108
 
1.6%
wenexban18 78
 
1.1%
Other values (29) 395
 
5.7%

Most occurring characters

ValueCountFrequency (%)
L 9780
14.2%
M 9144
13.3%
E 8789
12.8%
X 6906
10.0%
U 5347
7.8%
T 4957
7.2%
0 4657
6.8%
4 4429
6.4%
C 3067
 
4.5%
H 2287
 
3.3%
Other values (25) 9437
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 9780
14.2%
M 9144
13.3%
E 8789
12.8%
X 6906
10.0%
U 5347
7.8%
T 4957
7.2%
0 4657
6.8%
4 4429
6.4%
C 3067
 
4.5%
H 2287
 
3.3%
Other values (25) 9437
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 9780
14.2%
M 9144
13.3%
E 8789
12.8%
X 6906
10.0%
U 5347
7.8%
T 4957
7.2%
0 4657
6.8%
4 4429
6.4%
C 3067
 
4.5%
H 2287
 
3.3%
Other values (25) 9437
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 9780
14.2%
M 9144
13.3%
E 8789
12.8%
X 6906
10.0%
U 5347
7.8%
T 4957
7.2%
0 4657
6.8%
4 4429
6.4%
C 3067
 
4.5%
H 2287
 
3.3%
Other values (25) 9437
13.7%

customerNameCode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct39
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
Larsen & toubro limited
4092 
Ford india private limited
657 
Daimler india commercial vehicles pvt lt
588 
Lucas tvs ltd
 
222
Ashok leyland limited
 
211
Other values (34)
1110 

Length

Max length40
Median length23
Mean length24.977762
Min length13

Characters and Unicode

Total characters171847
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowAshok leyland limited
2nd rowDaimler india commercial vehicles pvt lt
3rd rowLucas tvs ltd
4th rowDaimler india commercial vehicles pvt lt
5th rowLucas tvs ltd

Common Values

ValueCountFrequency (%)
Larsen & toubro limited 4092
59.5%
Ford india private limited 657
 
9.5%
Daimler india commercial vehicles pvt lt 588
 
8.5%
Lucas tvs ltd 222
 
3.2%
Ashok leyland limited 211
 
3.1%
Ericsson india private limited 211
 
3.1%
Brakes india private ltd 161
 
2.3%
Tvs motor company limited 117
 
1.7%
Otis elevator company (india) ltd 108
 
1.6%
Wipro enterprises pvt ltd 78
 
1.1%
Other values (29) 435
 
6.3%

Length

2024-08-13T04:07:30.288550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
limited 5496
19.4%
4116
14.5%
larsen 4092
14.4%
toubro 4092
14.4%
india 1867
 
6.6%
private 1126
 
4.0%
ltd 782
 
2.8%
pvt 743
 
2.6%
ford 657
 
2.3%
daimler 588
 
2.1%
Other values (69) 4791
16.9%

Most occurring characters

ValueCountFrequency (%)
21470
12.5%
i 18397
10.7%
e 14543
 
8.5%
t 13890
 
8.1%
r 12472
 
7.3%
o 11103
 
6.5%
a 9616
 
5.6%
l 9335
 
5.4%
d 9100
 
5.3%
m 7886
 
4.6%
Other values (39) 44035
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21470
12.5%
i 18397
10.7%
e 14543
 
8.5%
t 13890
 
8.1%
r 12472
 
7.3%
o 11103
 
6.5%
a 9616
 
5.6%
l 9335
 
5.4%
d 9100
 
5.3%
m 7886
 
4.6%
Other values (39) 44035
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21470
12.5%
i 18397
10.7%
e 14543
 
8.5%
t 13890
 
8.1%
r 12472
 
7.3%
o 11103
 
6.5%
a 9616
 
5.6%
l 9335
 
5.4%
d 9100
 
5.3%
m 7886
 
4.6%
Other values (39) 44035
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21470
12.5%
i 18397
10.7%
e 14543
 
8.5%
t 13890
 
8.1%
r 12472
 
7.3%
o 11103
 
6.5%
a 9616
 
5.6%
l 9335
 
5.4%
d 9100
 
5.3%
m 7886
 
4.6%
Other values (39) 44035
25.6%

supplierID
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size53.9 KiB
Distinct309
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:30.812583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length49
Median length43
Mean length20.548692
Min length3

Characters and Unicode

Total characters141375
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)1.5%

Sample

1st rowVIJAY TRANSPORT
2nd rowVJ LOGISTICS
3rd rowG.S. TRANSPORT
4th rowARVINTH TRANSPORT
5th rowSR TRANSPORTS
ValueCountFrequency (%)
logistics 1208
 
6.0%
transport 1207
 
6.0%
private 955
 
4.8%
transports 877
 
4.4%
unknown 742
 
3.7%
a 708
 
3.5%
limited 705
 
3.5%
carriers 586
 
2.9%
s 544
 
2.7%
sunita 497
 
2.5%
Other values (424) 12062
60.0%
2024-08-13T04:07:31.649678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 14575
 
10.3%
13473
 
9.5%
A 13018
 
9.2%
T 11401
 
8.1%
S 10820
 
7.7%
I 9178
 
6.5%
N 6840
 
4.8%
E 6426
 
4.5%
P 4880
 
3.5%
O 4607
 
3.3%
Other values (47) 46157
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 14575
 
10.3%
13473
 
9.5%
A 13018
 
9.2%
T 11401
 
8.1%
S 10820
 
7.7%
I 9178
 
6.5%
N 6840
 
4.8%
E 6426
 
4.5%
P 4880
 
3.5%
O 4607
 
3.3%
Other values (47) 46157
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 14575
 
10.3%
13473
 
9.5%
A 13018
 
9.2%
T 11401
 
8.1%
S 10820
 
7.7%
I 9178
 
6.5%
N 6840
 
4.8%
E 6426
 
4.5%
P 4880
 
3.5%
O 4607
 
3.3%
Other values (47) 46157
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 14575
 
10.3%
13473
 
9.5%
A 13018
 
9.2%
T 11401
 
8.1%
S 10820
 
7.7%
I 9178
 
6.5%
N 6840
 
4.8%
E 6426
 
4.5%
P 4880
 
3.5%
O 4607
 
3.3%
Other values (47) 46157
32.6%
Distinct1407
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size53.9 KiB
2024-08-13T04:07:32.279479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length40
Median length35
Mean length17.204506
Min length3

Characters and Unicode

Total characters118367
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique652 ?
Unique (%)9.5%

Sample

1st rowBRACKET / GRAB HANDLE
2nd rowZB MODEL PLATE / 3143
3rd rowLETTERING / FUSO
4th rowLU STRUT RA / RADIUS ROD
5th rowWISHBONE / V ROD/HDT
ValueCountFrequency (%)
parts 1684
 
8.3%
auto 1516
 
7.4%
1093
 
5.4%
starter 555
 
2.7%
empty 515
 
2.5%
12v 441
 
2.2%
assy 426
 
2.1%
motor 396
 
1.9%
lu 307
 
1.5%
trays 266
 
1.3%
Other values (1572) 13200
64.7%
2024-08-13T04:07:33.103218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13808
 
11.7%
A 9938
 
8.4%
T 9741
 
8.2%
R 9002
 
7.6%
S 7645
 
6.5%
E 7307
 
6.2%
O 6192
 
5.2%
P 4416
 
3.7%
L 4383
 
3.7%
I 4124
 
3.5%
Other values (67) 41811
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 118367
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13808
 
11.7%
A 9938
 
8.4%
T 9741
 
8.2%
R 9002
 
7.6%
S 7645
 
6.5%
E 7307
 
6.2%
O 6192
 
5.2%
P 4416
 
3.7%
L 4383
 
3.7%
I 4124
 
3.5%
Other values (67) 41811
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 118367
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13808
 
11.7%
A 9938
 
8.4%
T 9741
 
8.2%
R 9002
 
7.6%
S 7645
 
6.5%
E 7307
 
6.2%
O 6192
 
5.2%
P 4416
 
3.7%
L 4383
 
3.7%
I 4124
 
3.5%
Other values (67) 41811
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 118367
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13808
 
11.7%
A 9938
 
8.4%
T 9741
 
8.2%
R 9002
 
7.6%
S 7645
 
6.5%
E 7307
 
6.2%
O 6192
 
5.2%
P 4416
 
3.7%
L 4383
 
3.7%
I 4124
 
3.5%
Other values (67) 41811
35.3%

Interactions

2024-08-13T04:07:09.217052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:06.542629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:07.474638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:08.337576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:09.854190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:06.754090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:07.685192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:08.537465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:10.081000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:07.011576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:07.969792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:08.770869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:10.293638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:07.231739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:08.140384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-13T04:07:09.019749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-08-13T04:07:33.278249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Curr_latCurr_lonDriver_MobileNoGpsProviderMarket/RegularMinimum_kms_to_be_covered_in_a_dayTRANSPORTATION_DISTANCE_IN_KMcustomerIDcustomerNameCodevehicleType
Curr_lat1.000-0.240-0.1210.2220.1190.1580.0230.2950.2940.231
Curr_lon-0.2401.0000.0480.1850.0620.1250.0130.3370.3360.317
Driver_MobileNo-0.1210.0481.0000.2821.0000.000-0.0310.1920.1910.189
GpsProvider0.2220.1850.2821.0000.0000.2890.3340.4330.4310.211
Market/Regular0.1190.0621.0000.0001.0001.0000.2060.4100.4251.000
Minimum_kms_to_be_covered_in_a_day0.1580.1250.0000.2891.0001.0000.3611.0001.0000.707
TRANSPORTATION_DISTANCE_IN_KM0.0230.013-0.0310.3340.2060.3611.0000.5050.5040.425
customerID0.2950.3370.1920.4330.4101.0000.5051.0000.9870.493
customerNameCode0.2940.3360.1910.4310.4251.0000.5040.9871.0000.503
vehicleType0.2310.3170.1890.2111.0000.7070.4250.4930.5031.000

Missing values

2024-08-13T04:07:10.799582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-13T04:07:11.765770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-13T04:07:12.379126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GpsProviderBookingIDMarket/RegularBookingID_Datevehicle_noOrigin_LocationDestination_LocationOrg_lat_lonDes_lat_lonData_Ping_timePlanned_ETACurrent_LocationDestinationLocationactual_etaCurr_latCurr_lonontimedelayOriginLocation_CodeDestinationLocation_Codetrip_start_datetrip_end_dateTRANSPORTATION_DISTANCE_IN_KMvehicleTypeMinimum_kms_to_be_covered_in_a_dayDriver_NameDriver_MobileNocustomerIDcustomerNameCodesupplierIDsupplierNameCodeMaterial Shipped
0CONSENT TRACKMVCV0000927/082021Market2020-08-17 14:59:01.000KA590408TVSLSL-PUZHAL-HUB,CHENNAI,TAMIL NADUASHOK LEYLAND PLANT 1- HOSUR,HOSUR,KARNATAKA13.1550,80.196012.7400,77.82002020-08-24 00:05:092020-08-21 18:59:01Vaniyambadi Rd, Valayambattu, Tamil Nadu 635752, IndiaASHOK LEYLAND PLANT 1- HOSUR,HOSUR,KARNATAKA2020-08-28 14:38:04.44700012.66350078.649870NaNRCHEPUZTVSHUA1HOSHOSALLCCA22020-08-17 14:59:01NaN320.0NaNNaNNaNNaNALLEXCHE45Ashok leyland limitedVIJEXHOSR7VIJAY TRANSPORTBRACKET / GRAB HANDLE
1VAMOSYSVCV00014271/082021Regular2020-08-27 16:22:22.827TN30BC5917DAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADUDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU12.8390,79.954012.8390,79.95402020-08-28 12:40:282020-08-31 20:22:22.827000Unnamed Road, Oragadam Industrial Corridor, Vattambakkam R.F., Tamil Nadu 631605, IndiaDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU2020-08-28 12:46:17.00700012.83675779.954428GNaNCHEORADMRCCB1CHEMATDMROPA72020-08-27 16:21:52NaN103.0NaNNaNRAMESHNaNDMREXCHEUXDaimler india commercial vehicles pvt ltVJLEXSHE09VJ LOGISTICSZB MODEL PLATE / 3143
2CONSENT TRACKVCV00014382/082021Regular2020-08-27 17:59:24.987TN22AR2748LUCAS TVS LTD-PONDY,PONDY,PONDICHERRYLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY11.8710,79.739011.8710,79.73902020-08-28 09:05:092020-08-31 21:59:24.987000570, National Hwy 48, Shenoy Nagar, Chennai, Tamil Nadu 600030, IndiaLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY2020-08-28 16:03:30.79300013.07395680.225780GNaNCHEPONLUTCCA4CHEPONLUTCCA42020-08-27 17:57:04NaN300.0NaNNaNGIRINaNLUTGCCHE06Lucas tvs ltdGSTEXLAK1QG.S. TRANSPORTLETTERING / FUSO
3VAMOSYSVCV00014743/082021Regular2020-08-28 00:48:24.503TN28AQ0781DAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADUDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU12.8390,79.954012.8390,79.95402020-08-28 12:40:312020-09-01 04:48:24.503000Singaperumal Koil - Sriperumbudur Rd, Oragadam Industrial Corridor, Vattambakkam R.F., Tamil Nadu 631605, IndiaDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU2020-08-28 12:50:27.99700012.83668679.950560GNaNCHEORADMRCCB1CHEMATDMROPA72020-08-28 00:47:45NaN61.0NaNNaNRAVINaNDMREXCHEUXDaimler india commercial vehicles pvt ltARVEXNAM09ARVINTH TRANSPORTLU STRUT RA / RADIUS ROD
4VAMOSYSVCV00014744/082021Regular2020-08-28 01:23:19.243TN68F1722LUCAS TVS LTD-PONDY,PONDY,PONDICHERRYLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY11.8720,79.632011.8720,79.63202020-08-28 12:40:292020-09-01 05:23:19.243000Melmaruvathur, Tamil Nadu 603319, IndiaLUCAS TVS LTD-PONDY,PONDY,PONDICHERRY2020-08-28 14:22:50.12700012.42950179.831556GNaNCHENETLUTCCA1CHENETLUTCCA12020-08-28 01:13:48NaN240.0NaNNaNTAMILNaNLUTGCCHE06Lucas tvs ltdSRTEXKOR96SR TRANSPORTSWISHBONE / V ROD/HDT
5VAMOSYSVCV00014749/082021Regular2020-08-28 02:14:22.640TN88A4980DAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADUDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU12.8390,79.954012.8390,79.95402020-08-28 12:40:282020-09-01 06:14:22.640000Ind.park Road, Nayapakkam, Tamil Nadu 602105, IndiaDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU2020-08-28 13:25:50.35300013.01736579.962313GNaNCHEORADMRCCB1CHEMATDMROPA72020-08-28 02:13:39NaN70.0NaNNaNGANESHNaNDMREXCHEUXDaimler india commercial vehicles pvt ltESWEXNAM02ESWAR TRANSPORTMOUNTING BRACKET / FUEL TANK
6VAMOSYSVCV00014750/082021Regular2020-08-28 02:20:27.530TN88C8204DAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADUDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU12.8390,79.954012.8390,79.95402020-08-28 12:40:292020-09-01 06:20:27.530000Rettai Kovil Bus Stop, 64, Salem - Ulundurpettai Hwy, K R Nagar, Seelanaickenpatti, Salem, Tamil Nadu 636006, IndiaDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU2020-08-28 17:38:13.48000011.62596278.157271GNaNCHEORADMRCCB1CHEMATDMROPA72020-08-28 02:19:47NaN931.0NaNNaNSAKTHIVEL.MNaNDMREXCHEUXDaimler india commercial vehicles pvt ltESWEXNAM02ESWAR TRANSPORTMOUNTING BRACKET / FUEL TANK
7VAMOSYSVCV00014812/082021Regular2020-08-28 09:22:31.377TN88D4133DAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADUDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU12.8390,79.954012.8390,79.95402020-08-28 12:40:282020-09-01 13:22:31.377000Singaperumal Koil - Sriperumbudur Rd, Oragadam Industrial Corridor, Vattambakkam R.F., Tamil Nadu 631605, IndiaDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU2020-08-28 12:49:03.72700012.83683779.951003GNaNCHEORADMRCCB1CHEMTADMROPA12020-08-28 09:21:56NaN20.0NaNNaNSUDHAKARNaNDMREXCHEUXDaimler india commercial vehicles pvt ltNSAEXNAMDZNAMAKKAL SRI ANJINAYA TRANSPORTMOUNTING BRACKET / FUEL TANK
8CONSENT TRACKMVCV0001769/082021Market2020-08-28 09:38:30.000TN23AM4662ASHOK LEYLAND ENNORE,CHENNAI,TAMIL NADUASHOK LEYLAND PLANT 2-HOSUR,HOSUR,KARNATAKA13.2150,80.320012.7660,77.78602020-08-28 09:00:062020-09-01 13:38:30Mumbai Hwy, Komeswaram, Tamil Nadu 635802, IndiaASHOK LEYLAND PLANT 2-HOSUR,HOSUR,KARNATAKA2020-08-28 15:00:19.08000012.81047078.740460GNaNCHEENNALLCCA1HOSHOSALLCCA32020-08-28 09:38:30NaN310.0NaNNaNNaNNaNALLEXCHE45Ashok leyland limitedVRLEXENN11VINAYAKA ROADLINESMOUNTING BRACKET / FUEL TANK
9VAMOSYSVCV00014665/082021Regular2020-08-27 22:27:54.427TN30BC5982DAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADUDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU12.8390,79.954012.8390,79.95402020-08-28 12:40:282020-09-01 02:27:54.427000Unnamed Road, Oragadam Industrial Corridor, Vattambakkam R.F., Tamil Nadu 631605, IndiaDAIMLER INDIA COMMERCIAL VEHICLES,KANCHIPURAM,TAMIL NADU2020-08-28 12:46:20.71700012.83794179.951967GNaNCHEORADMRCCB1CHEMATDMROPA72020-08-27 22:27:14NaN103.0NaNNaNSENTHIL KUMARNaNDMREXCHEUXDaimler india commercial vehicles pvt ltVJLEXSHE09VJ LOGISTICSMOUNTING BRACKET / FUEL TANK
GpsProviderBookingIDMarket/RegularBookingID_Datevehicle_noOrigin_LocationDestination_LocationOrg_lat_lonDes_lat_lonData_Ping_timePlanned_ETACurrent_LocationDestinationLocationactual_etaCurr_latCurr_lonontimedelayOriginLocation_CodeDestinationLocation_Codetrip_start_datetrip_end_dateTRANSPORTATION_DISTANCE_IN_KMvehicleTypeMinimum_kms_to_be_covered_in_a_dayDriver_NameDriver_MobileNocustomerIDcustomerNameCodesupplierIDsupplierNameCodeMaterial Shipped
6870JTECHWDSBKTP43133Regular2019-03-30 14:55:15KA01AF0114Mugabala, Bangalore Rural, KarnatakaKengeri, Bangalore Rural, Karnataka12.992699792116747,77.80384141110859312.73017358940589,77.956202036915672019-06-14 15:20:122019-03-30 17:17:00SGT Goods Shed Rd, Kadugodi, Bengaluru, Karnataka 560067, IndiaKengeri, Bangalore Rural, Karnataka2019-03-30 16:32:0012.99651677.740759GNaNVLR00777LE0055412019-03-30 15:35:002019-03-30 16:32:0047.340 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSBSHG-VLV GID
6871JTECHWDSBKTP42137Regular2019-03-20 18:57:16KA01AF0114Mugabala, Bangalore Rural, KarnatakaAnekal, Bangalore, Karnataka12.992699792116747,77.80384141110859312.711983972934837,77.691085933678482019-06-14 15:20:122019-03-20 20:48:16SGT Goods Shed Rd, Kadugodi, Bengaluru, Karnataka 560067, IndiaAnekal, Bangalore, Karnataka2019-06-14 16:57:00.13700012.99651677.740759NaNRVLR00777LE0048502019-03-20 18:57:162019-06-14 16:57:00.137000NaN40 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSSWITCH ASSEMBLY RH
6872JTECHWDSBKTP43018Regular2019-03-29 18:33:01KA01AF8395Mugabala, Bangalore Rural, KarnatakaAnekal, Bangalore, Karnataka16.560192249175344,80.79229309159954712.711983972934837,77.691085933678482019-06-14 15:20:122019-03-29 22:12:00NH 48, Chikkabennur, Karnataka 577541, IndiaAnekal, Bangalore, Karnataka2019-03-29 21:27:0014.31247776.246717GNaNV0048673LE0048502019-03-29 20:21:002019-03-29 21:27:0049.040 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSCONTROL LEVER ASSY L
6873JTECHWDSBKTP42532Regular2019-03-25 18:20:45KA21A6239Mugabala, Bangalore Rural, KarnatakaChikkabidarkal, Bangalore, Karnataka16.560192249175344,80.79229309159954712.777874729699617,77.6422755373470892019-06-14 15:20:122019-03-27 23:09:00Shed No 60, Medahalli Kadugodi Road, Virgonagar Industrial Estate, Aavalahalli, Bengaluru, Karnataka 560049, IndiaChikkabidarkal, Bangalore, Karnataka2019-03-27 22:24:0013.02486677.722378GNaNV0048673LE0051792019-03-27 20:03:002019-03-27 22:24:0031.040 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSKILL SWITCH
6874JTECHWDSBKTP45240Regular2019-04-23 12:32:42KA21A6239Mugabala, Bangalore Rural, KarnatakaBangalore International Airport, Bangalore, Karnataka16.560192249175344,80.79229309159954713.199089183304451,77.7085542349590382019-06-14 15:20:122019-04-24 15:40:00Shed No 60, Medahalli Kadugodi Road, Virgonagar Industrial Estate, Aavalahalli, Bengaluru, Karnataka 560049, IndiaBangalore International Airport, Bangalore, Karnataka2019-04-24 14:55:0013.02486677.722378GNaNV0048673LE0057702019-04-24 11:40:002019-04-24 14:55:0040.040 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSHORN NP.027
6875JTECHWDSBKTP42751Regular2019-03-27 17:25:33KA219502Ramamurthy Nagar, Bangalore, KarnatakaSahakaranagar P.O, Bangalore, Karnataka13.007503209603689,77.66509885593488613.068901840235711,77.5906557388066182019-06-14 15:20:122019-03-27 18:31:00SGT Goods Shed Rd, Kadugodi, Bengaluru, Karnataka 560067, IndiaSahakaranagar P.O, Bangalore, Karnataka2019-03-27 17:46:0012.99637077.740616GNaNV0045771LE0058702019-03-27 18:00:002019-03-27 17:46:0012.025 FT Open Body 21MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSTOOL KIT SET
6876JTECHWDSBKTP43203Regular2019-03-31 15:02:34KA01AE9163Ramamurthy Nagar, Bangalore, KarnatakaBangalore International Airport, Bangalore, Karnataka13.007503209603689,77.66509885593488613.196312912801169,77.7081569256887262019-06-14 15:20:122019-03-31 20:36:00Shed No 60, Medahalli Kadugodi Road, Virgonagar Industrial Estate, Aavalahalli, Bengaluru, Karnataka 560049, IndiaBangalore International Airport, Bangalore, Karnataka2019-03-31 19:51:0013.02478877.722391GNaNV0045771LE0054192019-03-31 17:30:002019-03-31 19:51:0031.040 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSCONTROL LEVER ASSY
6877JTECHWDSBKTP43021Regular2019-03-29 18:56:26KA01AE9163Mugabala, Bangalore Rural, KarnatakaAnekal, Bangalore, Karnataka16.560192249175344,80.79229309159954712.722686,77.6765182019-06-14 15:20:122019-03-30 01:38:00Shed No 60, Medahalli Kadugodi Road, Virgonagar Industrial Estate, Aavalahalli, Bengaluru, Karnataka 560049, IndiaAnekal, Bangalore, Karnataka2019-03-30 00:53:0013.02478877.722391GNaNV0048673LE0058482019-03-29 20:44:002019-03-30 00:53:0049.040 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSSPARE PARTS AUTOMOBILE
6878JTECHWDSBKTP42685Regular2019-03-27 08:29:45KA21A3643Mugabala, Bangalore Rural, KarnatakaAnekal, Bangalore, Karnataka16.560192249175344,80.79229309159954712.896896847817695,77.7122230568748622019-06-14 15:20:122019-03-27 17:20:00Shed No 60, Medahalli Kadugodi Road, Virgonagar Industrial Estate, Aavalahalli, Bengaluru, Karnataka 560049, IndiaAnekal, Bangalore, Karnataka2019-03-27 16:35:0013.02474777.721823NaNRV0048673LEL045802019-03-27 15:29:002019-03-27 16:35:0049.040 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSSPARE PARTS AUTOMOBILE
6879JTECHWDSBKTP42858Regular2019-03-28 17:55:17KA51D1317Mugabala, Bangalore Rural, KarnatakaAnekal, Bangalore, Karnataka16.560192249175344,80.79229309159954713.199089183304451,77.7085542349590382019-06-14 15:20:122019-03-29 00:26:0020/1, Sulthangunta, Shivaji Nagar, Bengaluru, Karnataka 560051, IndiaAnekal, Bangalore, Karnataka2019-03-28 23:41:0012.99181577.606038GNaNV0048673LE0057702019-03-28 20:26:002019-03-28 23:41:0049.040 FT 3XL Trailer 35MTNaNNaNNaNLTLEXMUM40Larsen & toubro limited55556A S TRANSPORTSSPARE PARTS AUTOMOBILE